Method for segmenting images, computer program, and corresponding computer system

ABSTRACT

An image segmenting method includes: reading ( 202 ) an image, determining ( 232 ) a solution to the problem of maximum flow in a graph including, on the one hand, as vertices, a source, a sink and image points, with each point being assigned a capacity, called a through-capacity, assigning ( 234 ), on the basis of the determined solution, a label to each of at least some of the points of the image, and recording the image with the assigned labels in a computer memory. In addition, before determining a solution to the problem of maximum flow, the method includes: determining ( 212 ) critical points, for each of which, the points of the image located in a predetermined window applied around the critical point verify a predetermined condition on their through-capacities. The points of the graph include the determined critical points and the inter-point arcs link the neighboring critical points to one another.

The present invention relates to an image segmenting method, acorresponding computer program and a corresponding computer system.

The invention applies more specifically to the field of computer vision.

The following terminology is used in the present description, as well asin the claims.

An “computer system” is a system designed to run computer programinstructions and, to this end, comprising at least one centralprocessing unit, also called a processor or a CPU. In order ofincreasing complexity, a computer system according to the invention mayconsist of a single computer comprising one or more CPU(s), or a morecomplex assembly in which a plurality of computers are interconnectedvia a data transmission network.

A “computer memory” means any type of medium on which data capable ofbeing read by a computer is recorded. This term covers in particularfloppy disks, compact disks or DVDs. This term also covers systemscombining the actual computer memory and the devices for reading and/orwriting on the latter, such as RAM memory, hard disks or USB flashdrives.

A “computer program”, or more simply a “program”, is a set ofinstructions intended to be run by one or more processors of a computersystem.

The article of Y. Boykov and M-P. Jolly: “Interactive graph cuts foroptimal boundary and region segmentation of objects in n-d images”,ICCV01: Proceedings of “International Conference on Computer Vision”,pages 105-112, 2001, describes an image processing method comprisingsteps of:

-   -   reading an image recorded in a computer memory, wherein the        image comprises a plurality of points, each having a position in        the image,    -   determining a solution to the problem of maximum flow in a graph        comprising, on the one hand, as vertices, a source, a sink and        image points, and, on the other hand, arcs each having a        positive or zero flow passage capacity,

wherein the arcs include arcs called source arcs, linking the source toeach of the points, arcs called sink arcs, linking the sink to each ofthe points, and arcs called inter-point arcs, linking the points to oneanother,

wherein each point is assigned a capacity, called a through-capacity,representing the combination of the flow passage capacities of thesource arc and the sink arc linking it respectively to the source and tothe sink,

-   -   assigning, on the basis of the determined solution, an “object”        label or a “background” label to each of at least some of the        points of the image,    -   recording the image with the assigned labels in a computer        memory.

In this document, the image is two-dimensional with its points regularlyspaced apart in the two dimensions and distributed over rows andcolumns.

In addition, the label of certain image points, called seeds, isassigned by the user or determined by the computer.

The vertices of the graph are constituted by all of the image points andthe arcs of the graph link each point with its neighboring points,according to the so-called “type-0” neighboring criterion. Thiscriterion defines, in two dimensions, the neighboring points of a pointconsidered, as the four points positioned respectively directly above,directly below, directly to the left and directly to the right of thepoint considered.

A capacity, called an inter-point capacity, is assigned to each of thesearcs. The inter-point capacity is determined on the basis of theintensities of the two neighboring points linked by the arc.

In addition, a through-capacity is assigned to each image point. Thisthrough-capacity is determined differently depending on whether thepoint is a seed point or an unknown label point. In the latter case, thethrough-capacity is determined on the basis of the intensity of thepoint.

The problem of maximum flow, known in itself, consists of determiningthe maximum “flow” capable of flowing from a source to a sink, bothlinked to each point of the graph, complying, in the graph, with theinter-point capacities and, across the graph, with thethrough-capacities.

A number of algorithms are available for solving this problem, such asthat presented in the article of Y. Boykov and V. Kolmogorov: “Anexperimental comparison of min-cut/max flow algorithms for energyminimization in vision”, EMMCVPR01 Proceedings of the InternationalWorkshop on Energy Minimization Methods in Computer Vision and PatternRecognition, pages 359-374, 2001.

It is empirically observed that this algorithm requires an amount ofcomputer memory and a calculation time that increase linearly as afunction of the number of vertices and arcs in the graph.

Thus, the known segmenting methods present the problem of requiringlarge amounts of computer memory and computer calculation time, when thesize of the image, i.e. the number of points that it has, increases.

It may thus be desirable to provide an image segmenting method thatenables the aforementioned problems and constraints to be at leastpartially overcome.

The invention therefore relates to an image segmenting method,comprising steps of:

-   -   reading an image recorded in a computer memory, wherein the        image comprises a plurality of points, each having a position in        the image,    -   determining a solution to the problem of maximum flow in a graph        comprising, on the one hand, as vertices, a source, a sink and        image points, and, on the other hand, arcs each having a        positive or zero flow passage capacity,

wherein the arcs include arcs called source arcs, linking the source toeach of the points, arcs called sink arcs, linking the sink to each ofthe points, and arcs called inter-point arcs, linking the points to oneanother,

wherein each point is assigned a capacity, called a through-capacity,representing the combination of the flow passage capacities of thesource arc and the sink arc linking it respectively to the source and tothe sink,

-   -   assigning, on the basis of the determined solution, an “object”        label or a “background” label to each of at least some of the        points of the image,    -   recording the image with the assigned labels in a computer        memory,

characterized in that it comprises, before determining a solution to theproblem of maximum flow:

-   -   determining points of the image, called critical points, for        each of which the image points located in a predetermined window        applied around the critical point verify a predetermined        condition on their through-capacities,

and in that the points of the graph include the determined criticalpoints and the inter-point arcs linking the neighboring critical pointsto one another according to a predetermined neighboring criterion ontheir relative positions in the image.

Indeed, it has been found that, most of the time, the solution to theproblem of maximum flow on all of the points of the image gave anon-zero flow in the source arc or in the sink arc for each of only asmall number of points. It was also found that it was possible todetermine all of these points, or at least a large portion of thesepoints, in advance (i.e. before solving the problem of maximum flow), onthe basis of the through-capacities. More specifically, it was foundthat a point had a good chance of having a non-zero flow in its sourcearc or its sink arc if the points of the image located in apredetermined window applied around the critical point verified apredetermined condition on their through-capacities. Thus, in the bestof cases, the critical points found correspond exactly to the pointsthat would have had a non-zero flow in the source arc or in the sink arcwith a solution of the maximum flow on all of the points of the image.Of course, it is possible, depending on the condition chosen, for thecritical points not to correspond exactly with these points: some mighthave a zero through-flow while solving the problem of maximum flow onall of the points of the image, and, conversely, some points that wouldhave had a non-zero through-flow by solving the problem of maximum flowon all of the points of the image may not be determined to be a criticalpoint.

Owing to the invention, the problem of maximum flow is solved only forsome points of the image (the critical points), which in generalrepresent only a small portion of all of the points of the image. Thus,the amount of computer memory and the computer calculation timenecessary for segmentation are reduced.

In addition, it has been observed that the solution to the problem ofmaximum flow at only the critical points gives very good segmentingresults, close to the solution that would have been obtained by takinginto account all of the points of the image.

Optionally, all of the points of the image verifying the predeterminedcondition are determined.

Also optionally, the window is centered over the point to which it isapplied.

Also optionally, each point has an intensity, the through-capacity ofeach of at least some of the points is determined on the basis of theintensity of this point, and the flow passage capacity of each of atleast some of the inter-point arcs is determined on the basis of theintensity of the two points that it links.

Also optionally, the predetermined condition is that, among at least apredetermined number of points of the image located in a predeterminedwindow applied around the critical point:

-   -   at least two points have through-capacities of opposite signs,    -   at least one point has a positive through-capacity that is less        than a predetermined positive threshold, or    -   at least one point has a negative through-capacity that is        greater than a predetermined negative threshold.

Indeed, with well-chosen capacity thresholds (i.e. relatively large inabsolute value), a point that is not critical means that the maximumflow quantity capable of entering (or leaving, respectively) the windowaround this point, and without considering this point, is greater thanthe maximum flow quantity capable of leaving (or entering,respectively). There is therefore a high probability that the points ofthe window around the point considered are enough to saturate theinter-point arcs of the periphery of the window, and therefore that themaximum flow solution would give a zero flow from the source (or thesink, respectively) to this point, i.e. this point is not involved inthe maximum flow found. Experimentally, it has been found that, withcertain threshold values, identical solutions between the method of theinvention and the method with all of the points of the image wereobtained. It has also been observed that the segmentation changes as theabsolute values of the thresholds decrease.

Also optionally, the thresholds are constant in the window.

Also optionally, the method comprises, in order to determine thecritical points: examining the points of the image, and, for each pointexamined, called a point in progress: determining whether thethrough-capacities for at least a predetermined number of windowedpoints of the point in progress are each greater than the predeterminedpositive capacity threshold, or whether their through-capacities areeach less than the predetermined negative capacity threshold, byexamining all of the windowed points in order to compare theirthrough-capacity with the negative threshold and the positive threshold.

Also optionally, the method also comprises, in order to determine thecritical points: examining the points of the image, and, for each pointexamined, called a point in progress: determining whether thethrough-capacities for all of the windowed points of the point inprogress are each greater than the predetermined positive capacitythreshold, or whether their through-capacities are each less than thepredetermined negative capacity threshold, by using the determinationobtained for a point examined before the point in progress.

The invention also relates to a computer program capable of beingdownloaded from a communication network and/or recorded on a mediumcapable of being read by a computer, characterized in that it includescomputer program instructions for implementing an image segmentingmethod of the invention when said computer program is run by a computer.

The invention also relates to an information system comprising:

-   -   a device for reading a computer memory,    -   a device for writing in a computer memory,

characterized in that it comprises:

-   -   a computer memory in which a computer program is recorded,        including computer program instructions for, when the computer        program is run by the computer system:        -   reading, by means of the reading device, an image recorded            in a computer memory, wherein the image comprises a            plurality of points, each including a position in the image,        -   assigning a capacity to each point, called a            through-capacity,        -   determining points of the image, called critical points, for            each of which, the points of the image located in a            predetermined window applied around the critical point            verify a predetermined condition on their            through-capacities,        -   determining a solution to the problem of maximum flow in a            graph comprising, on the one hand, as vertices, a source, a            sink and determined image points, and, on the other hand,            arcs each having a positive or zero flow passage capacity,        -   wherein the arcs include arcs called source arcs, linking            the source to each of the points, arcs called sink arcs,            linking the sink to each of the points, and arcs called            inter-point arcs, linking the neighboring critical points to            one another according to a predetermined neighboring            criterion on their relative positions in the image,        -   wherein the through-capacities represent the combination of            the flow passage capacities of the source ark and the sink            arc linking it respectively to the source and to the sink,        -   assigning, on the basis of the determined solution, an            “object” label or a “background” label to each of at least            some of the points of the image,        -   recording, by means of the writing device, the image with            the assigned labels in a computer memory.

In particular, the computer system of the invention may be a medicalimaging machine, such as a resonance imaging machine (or MRI), ascanner, and so on.

The invention will be easier to understand in view of the followingdescription, provided solely by way of an example and in reference tothe appended drawings, in which:

FIG. 1 shows an example of a computer system according to the invention,

FIG. 2 shows the steps of an image segmenting method implemented by thecomputer system of FIG. 1,

FIG. 3 shows an example of the performance of some of the method of FIG.2,

FIG. 4 shows the steps of an alternative of the segmenting method ofFIG. 2, and

FIG. 5 shows an example of the performance of some of the method of FIG.4.

In reference to FIG. 1, an example of a computer system according to theinvention is shown, in the form of a computer 100.

The computer 100 comprises, first, a CPU 102 and RAM memory 104, capableof running computer program in order, in particular, to control theother elements of the computer 100.

Among these other elements, the computer 100 comprises, first, a devicefor reading 106 a computer memory and a device for writing 108 in acomputer memory. For example, these two devices 106, 108 correspond to ahard disk of the computer, or, respectively, to a compact disk or DVDreader and to a hard disk. In the remainder of the description, we willconsider the case of a hard disk, referenced 110, so that the twodevices 106, 108 access the same computer memory, referenced 112. Animage 114 is recorded in the computer memory 112 of the hard disk 110.This image comes, for example, from a medical imaging machine, such as anuclear magnetic resonance imaging machine (or nMRI), a scanner, and soon.

The computer 100 also comprises a computer memory in which a computerprogram 116 is recorded. In the remainder of the description, theprogram 116 will be considered to be recorded in the computer memory 112of the hard disk 110.

The computer 100 also comprises a human/machine interface 118, such asthe combination of a screen, a keyboard and/or a mouse.

The image 114 comprises a plurality of points, each including a positionin the image and an intensity. The intensity may comprise a singlevalue, representing, for example, a gray level, or a plurality ofvalues, representing, for example, a color, for example expressed in RGB(Red, Green, Blue) form or the values of a plurality of channels in thecase of a multi-channel image. In the remainder of the description, wewill consider the case of a two-dimensional square image, in which thepoints are regularly spaced apart according to the two dimensions, andorganized in rows and columns. All of the points are noted P.

The program 116 includes computer program instructions for, when thisprogram 116 is run by the computer system 100, implementing the imagesegmenting method, which will now be described.

In reference to FIG. 2, the image segmenting method 200 comprises thefollowing steps.

After, for example, the program 116 has been started by a user, in astep 202, the computer 100 reads, by means of the reading device 106,the image 114 recorded in the computer memory 112 of the hard disk 110.

In a step 204, the computer 100 will assign an “object” or “background”label to certain points, called “seed points”. These labels are, forexample, indicated by the user via the human/machine interface 118, orare determined by the computer 100 by means of an ad hoc method enablingthe seed points to be generated automatically.

In a step 206, the computer 100 determines a capacity, called athrough-capacity, for each point of the image. The through-capacity of apoint pεP is noted c(p). For the points that are not seed points, thisthrough-capacity is, for example, determined on the basis of theintensity of the point alone, without using the intensity of otherpoints.

In a step 208, the computer 100 assigns, to each point pεP, itsthrough-capacity c(p).

In a step 210, the computer 100 determines points of the image, calledcritical points.

A point of the image is a critical point when at least a predeterminednumber of points of the image located in a predetermined window appliedaround this point verify a predetermined condition on theirthrough-capacities.

The points of the image located in the predetermined window appliedaround a point pεP are called window points of this point p and arenoted Z(p). It should be noted that the point p around which the windowis applied, is itself a windowed point.

The fact of not considering all of the windowed points may bebeneficial, for example, when it is desirable to move away from thewindowed points those of which the intensity is determined to beaberrant. Preferably, however, a point of the image is a critical pointwhen all of the points of the image located in a predetermined windowapplied around this point verify the predetermined condition on theirthrough-capacities. It is the latter case that will be considered in theremainder of the description.

Preferably, the condition is that, among the windowed points of thepoint:

-   -   at least two windowed points have through-capacities of opposite        signs,    -   at least one windowed point has a positive through-capacity that        is less than a predetermined positive threshold, called a        positive capacity threshold, or    -   at least one windowed point has a negative through-capacity that        is greater than a predetermined negative threshold, called a        negative capacity threshold.

Thus, in other words, a point of the image is not a critical point whenthe through-capacities of all of the windowed points of this point:

-   -   are each greater than the predetermined positive capacity        threshold, or    -   are each less than the predetermined negative capacity        threshold.

Preferably, the window has a fixed size. Also preferably, the window iscentered over the point to which it is applied. Also preferably, thewindow is rectangular, for example square. Throughout the description,the window is considered to have a fixed size, to be centered and to besquare.

In addition, preferably, the capacity thresholds are equal in absolutevalue. Preferably, the capacity thresholds are constant, i.e.independent of the position of the point in the window, as well as thepoint to which the window is applied.

In the remainder of the description, the capacity thresholds areconsidered to be equal in absolute value and constant. They arerespectively noted δ and −δ (δ being strictly positive).

Thus, a point of the image is a critical point when it does not verifythe following condition, noted (C):

$\left\{ {\begin{matrix}{{{c(q)} \geq \delta},{\forall{q \in {Z(p)}}}} \\{or} \\{{{c(q)} \leq {- \delta}},{\forall{q \in {Z(p)}}}}\end{matrix}\quad} \right.$

Preferably, all of the points of the image verifying the predeterminedcondition are determined in step 210. The set of critical points ishereinafter noted P_(c).

In a step 212, the computer 100 determines, for each critical pointpεP_(c), its neighboring points, noted N(p), according to apredetermined neighboring criterion on their relative positions in theimage 114. In the example described, the neighboring criterion is theso-called type-0 neighboring criterion. This criterion defines, in twodimensions, the neighboring points of a point considered as the fourpoints positioned respectively directly above, directly below, directlyto the left and directly to the right of the point considered. Ingeneral, for an image of dimension d, the type-0 neighboring criterionis as follows:

${{N(p)} = \left\{ {{q \in {P\text{:}\mspace{14mu}{\sum\limits_{i = 1}^{d}\;{{q_{i} - p_{i}}}}}} = 1} \right\}},{\forall{p \in {P.}}}$

In a step 212, the computer 100 assigns, to each pair of neighboringcritical points, a capacity, called in inter-point capacity. Theinter-point capacity of two neighboring critical points pεP_(c) andqεP_(c) is noted c(p, q). This inter-point capacity is, for example,determined on the basis of the intensities of the two points alone,without using the intensity of other points.

For example, steps 210 and 212 are implemented at the same time by thenext steps 216 to 222.

In reference to FIGS. 2 and 3, in a step 216, the computer 100 resetsall of the elements of a list of size N (N being the side of the image),noted L, to the value −1, and sets a counter, noted cpt, to the value 0.

The computer 100 then examines the points of the image, preferably allof them, going row-by-row, from one point to the next. For each pointexamined, called point in progress, the computer 100 implements nextsteps 218 to 230.

In a step 218, the computer 100 determines whether the condition (C) isverified for the point in progress.

For this, in a step 220, the computer 100 examines all of the windowedpoints of the point in progress in order to compare theirthrough-capacity with each of the capacity thresholds.

If condition (C) is verified, in a step 222, the computer 100 willrecord the value −1 in the element L[i], i being the column number ofthe point in progress.

If condition (C) is not verified, the computer 100 implements the nextsteps.

In a step 224, the computer 100 identifies the point in progress asbeing the critical point.

In a step 226, the computer 100 identifies the point in progress and itsneighboring point located directly above as two adjacent critical pointsif the element L[i] has a value greater than or equal to zero. Thecomputer 100 then creates, always during this step, an arc between thesetwo neighboring critical points, then determines and associates aninter-point capacity with this arc.

In step 228, the computer 100 identifies the point in progress and itsneighboring point located directly to the left as two neighboringcritical points if the element L[i−1] has a value greater than or equalto zero. The computer 100 then creates, still during this step, an arcbetween these two neighboring critical points, then determines andassociates an inter-point capacity with this arc.

In a step 230, the computer 100 records the value of the counter cpt inthe element L[i] of the list, then increments the counter cpt by 1.

Thus, after steps 210 and 212, the computer 100 has determined all ofthe critical points of the image, as well as the pairs of neighboringcritical points.

In a step 232, the computer 100 determines a solution to the problem ofmaximum flow in a graph comprising, on the one hand, as vertices, asource, a sink and the determined image points, and, on the other hand,arcs each having a positive or zero flow passage capacity. These arcsinclude arcs called source arcs, linking the source to each of thecritical points, arcs called sink arcs, linking the sink to each of thepoints, and arcs called inter-point arcs, linking the points to oneanother. In this graph, the through-capacities represent the combinationof the flow passage capacities of the source arc and the sink arclinking it respectively to the source and to the sink. Morespecifically, the through-capacity of a point pεP is equal to thedifference in the flow passage capacities of the sink arc linking thispoint:c(p)=c _(source)(p)−c _(sink)(p)

As shown in the article of V. Kolmogorov and R. Zabih “What energyfunctions can be minimized via graph cuts?” published in ECCV02:Proceedings of the European Conference on Computer Vision, pages 65-81,2002, these two ways of seeing the through-flows are equivalent. Thus,in the sense of the invention, the term “through-capacity” means boththe value c(p) and the set of two values c_(source)(p) and c_(sink)(p).

The solution to this problem is obtained, for example, by using knownmethods, as described in the document cited above.

In a step 234, the computer 100 assigns, according to the solutiondetermined, an “object” label or a “background” label to each of atleast some of the points of the image, and preferably all of the pointsof the image.

In a step 236, the computer 100 records, in the computer memory 112 ofthe hard disk 110, by means of the writing device 108, the image 114with the assigned labels.

In the method of FIG. 2, the step 218 requires all of the windowedpoints to be examined, resulting in a high calculation cost,proportional to the size of the window. However, it may be beneficial touse a large window, because it has been found that this enabled thecapacity thresholds to be reduced significantly, in absolute value, andtherefore the number of critical points to be further reduced.

In reference to FIGS. 4 and 5, the method 300 overcomes this problem.

The method 300 is identical to the method 200 of FIG. 2, except thatstep 218 is replaced by the next steps 302 to 312, and, in the exampledescribed, the window is of an uneven number.

In a step 302, the computer 100 determines whether the condition (C) isverified for the point in progress, using the determination obtained forat least one point examined before the point in progress. This indeedmakes it possible to take advantage of the comparisons with the positiveand negative thresholds, which were previously produced.

Preferably, it is the next-to-last point that is used, i.e. thatexamined just before the point in progress. Indeed, in any case at adistance from the edges of the image, when going from one point to thenext in a row, only one windowed point has not been tested with respectto the capacity thresholds.

Thus, in a step 304, the computer 100 determines at least one windowedpoint, called the remaining windowed point, of which thethrough-capacity has not yet been compared with the capacity thresholds.

In a step 306, the computer 100 compares the through-capacity of eachremaining windowed point with the capacity thresholds.

In a step 308, the computer 100 assigns, to each remaining windowedpoint, a first test value when its through-capacity is greater than thepredetermined positive threshold, a second test value when itsthrough-capacity is less than the predetermined negative threshold, or athird test value in the other cases, with this third value being betweenthe first and second test values and different from them.

For example, the first, second and third test values are, respectively,1, −1 and 0. In this case, the test value of a remaining windowed pointpεP, noted g_(δ)(p), is given by:

${g_{\delta}(p)} = \left\{ {\begin{matrix}1 & {{{if}\mspace{14mu}{c(p)}} \geq \delta} \\{- 1} & {{{if}\mspace{14mu}{c(p)}} \leq {- \delta}} \\0 & {{if}\mspace{14mu}{not}}\end{matrix}\quad} \right.$

In a step 310, the computer 100 determines the sum of the test values ofthe windowed points of the point in progress, called the sum in progressand noted s(i, j), with i and j respectively being the column and rownumbers of the point in progress. The sum in progress is determined onthe basis of the sum of the test values of the windowed points of thepoint previously examined in the row, noted s(i−1, j), the test value ofthe remaining windowed point, and windowed points of the next-to-lastpoint no longer appearing among the windowed points of the point inprogress. The sum in progress is, for example, calculated by ((k−1)/2being noted k/2):s(i, j)=s(i−1, j)−M[i− k/2−1]+M[i+ k/2]

-   -   with        M[i+ k/2]=M[i+ k/2]−g _(δ)(i+ k/2, j− k/2−1)+g _(δ)(i+ k/2, j+        k/2)

In a step 312, the computer 100 determines whether the condition (C) isverified for the point in progress, and therefore whether the point inprogress is a critical point, based on the sum in progress s(i, j). Inthe example described, the point in progress is identified as being acritical point if |s (i, j)|≠k².

Steps 304 to 312 are repeated for each point examined, being adapted inthe case of points close to the edges of the image.

The method of FIG. 4 therefore makes it possible to overcome the problemof window size, so that the cost becomes almost constant according tothe window size.

It appears to be clear that the invention makes it possible to reducethe number of points of the graph for which the problem of maximum flowis solved, and, therefore, the calculation and computer memory costs ofthe segmenting method.

It should also be noted that the invention is not limited to theembodiments described above. It will indeed be apparent to a personskilled in the art that various modifications can be made to theembodiment described above, in light of the teaching presentlydisclosed.

First, the image is not limited to two dimensions. The image may, forexample, be three-dimensional (volume) or four dimensional (generallyrepresenting a volume over time), or even more. The methods presentedabove can easily be adapted to these cases.

In addition, the neighboring criterion may be chosen differently, forexample, as the type-1 neighboring criterion, in which the neighbors ofa point are defined by:N(p)={qεP:|q _(i) −p _(i)|≦1, ∀1≦i≦d}, ∀pεP.

Again, the methods presented above can easily be adapted. It is notedthat, with this kind of neighboring, the method of FIG. 2 requires alist of size N+1, instead of N, so that the computer memory cost isidentical.

In addition, it is possible to provide a window of which the size variesaccording to the image point to which it is applied.

In addition, the invention also covers the case in which the capacitythresholds vary according to the position of the point in the window.For example, it may be advantageous to provide capacity thresholds witha higher absolute value at the periphery of the window, since it is thewindowed points of the periphery of the window that have the highestprobability of contributing the most to the saturation of inter-pointarcs of the periphery of the window. However, in this case, there wouldin principle be fewer points determined as being critical points, andthe solving of the maximum flow problem would risk to be done on alarger graph.

In addition, in other embodiments, the image may be read and recordedusing computer memories of other types than a hard disk memory.

For example, the image may be read from the random access memory of thecomputer, or from a memory of an information device outside thecomputer, such as a medical imaging machine (MRI, scanner, etc.), via adata communication network.

For example, the image with its labels may be recorded in the randomaccess memory of the computer by means of corresponding writing devices.Thus, the image may be read quickly in order, for example, to performother processing operations, for example, in order to determine thevolume of the segmented object.

In the claims below, the terms used should not be interpreted aslimiting the claims to the embodiment described in this description, butshould be interpreted so as to include all of the equivalents that theclaims are intended to cover, in their wording and that can be envisagedby a person skilled in the art applying his or her general knowledge tothe implementation of the teaching disclosed above.

The invention claimed is:
 1. Image segmenting method, comprising: a computer system reading an image recorded in a computer memory, wherein the image comprises a plurality of points, each having a position in the image, a computer system determining a solution to the problem of maximum flow in a graph comprising, on the one hand, as vertices, a source, a sink and image points, and, on the other hand, arcs each having a positive or zero flow passage capacity, wherein the arcs include arcs called source arcs, linking the source to each of the points, arcs called sink arcs, linking the sink to each of the points, and arcs called inter-point arcs, linking the points to one another, wherein each point is assigned a capacity, called a through-capacity, representing the combination of the flow passage capacities of the source arc and the sink arc linking it respectively to the source and to the sink, a computer system assigning, on the basis of the determined solution, an “object” label or a “background” label to each of at least some of the points of the image, a computer system recording the image with the assigned labels in a computer memory, characterized in that it comprises, before determining a solution to the problem of maximum flow: a computer system determining points of the image, called critical points, for each of which the image points located in a predetermined window applied around the critical point verify a predetermined condition on their through-capacities, and in that the points of the graph include the determined critical points and the inter-point arcs linking the neighboring critical points to one another according to a predetermined neighboring criterion on their relative positions in the image.
 2. Image segmenting method according to claim 1, wherein all of the points of the image verifying the predetermined condition are determined.
 3. Image segmenting method according to claim 1, wherein the window is centered on the point to which it is applied.
 4. Image segmenting method according to claim 1, wherein: each point has an intensity, the through-capacity of each of at least some of the points is determined on the basis of the intensity of this point, and the flow passage capacity of each of at least some of the inter-point arcs is determined on the basis of the intensity of the two points that it links.
 5. Image segmenting method according to claim 1, wherein the predetermined condition is that, among at least a predetermined number of points of the image located in a predetermined window applied around the critical point: at least two points have through-capacities of opposite signs, at least one point has a positive through-capacity that is less than a predetermined positive threshold, or at least one point has a negative through-capacity that is greater than a predetermined negative threshold.
 6. Image segmenting method according to claim 5, in which the thresholds are constant in the window.
 7. Image segmenting method according to claim 5, also comprising, in order to determine the critical points: a computer system examining the points of the image, for each point examined, called a point in progress: a computer system determining whether the through-capacities for at least a predetermined number of windowed points of the point in progress: are each greater than the predetermined positive capacity threshold, or are each less than the predetermined negative capacity threshold, by examining all of the windowed points in order to compare their through-capacity with the negative threshold and the positive threshold.
 8. Image segmenting method according to claim 5, also comprising, in order to determine the critical points: a computer system examining the points of the image, for each point examined, called a point in progress: a computer system determining whether the through-capacities for all of the windowed points of the point in progress: are each greater than the predetermined positive capacity threshold, or are each less than the predetermined negative capacity threshold, by using the determination obtained for a point examined before the point in progress.
 9. A non-transitory computer-readable medium on which a computer program is recorded, characterized in that the computer program includes computer program instructions for implementing an image segmenting method according to claim 1 when said computer program is run by a computer.
 10. Computer system comprising: a device for reading a computer memory, a device for writing in a computer memory, characterized in that it comprises: a computer memory in which a computer program is recorded, including computer program instructions for, when the computer program is run by the computer system: reading, by means of the reading device, an image recorded in a computer memory, in which the image comprises a plurality of points, each including a position in the image, assigning a capacity to each point, called a through-capacity, determining points of the image, called critical points, for each of which, the points of the image located in a predetermined window applied around the critical point verify a predetermined condition on their through-capacities, determining a solution to the problem of maximum flow in a graph comprising, on the one hand, as vertices, a source, a sink and determined image points, and, on the other hand, arcs each having a positive or zero flow passage capacity, wherein the arcs include arcs called source arcs, linking the source to each of the points, arcs called sink arcs, linking the sink to each of the points, and arcs called inter-point arcs, linking the neighboring critical points to one another according to a predetermined neighboring criterion on their relative positions in the image, wherein the through-capacities represent the combination of the flow passage capacities of the source ark and the sink arc respectively linking it to the source and to the sink, assigning, on the basis of the determined solution, an “object” label or a “background” label to each of at least some of the points of the image, recording, by means of the writing device, the image with the assigned labels in a computer memory.
 11. Image segmenting method according to claim 2, wherein: each point has an intensity, the through-capacity of each of at least some of the points is determined on the basis of the intensity of this point, and the flow passage capacity of each of at least some of the inter-point arcs is determined on the basis of the intensity of the two points that it links.
 12. Image segmenting method according to claim 6, also comprising, in order to determine the critical points: a computer system examining the points of the image, for each point examined, called a point in progress: a computer system determining whether the through-capacities for at least a predetermined number of windowed points of the point in progress: are each greater than the predetermined positive capacity threshold, or are each less than the predetermined negative capacity threshold, by examining all of the windowed points in order to compare their through-capacity with the negative threshold and the positive threshold.
 13. Image segmenting method according to claim 6, also comprising, in order to determine the critical points: a computer system examining the points of the image, for each point examined, called a point in progress: a computer system determining whether the through-capacities for all of the windowed points of the point in progress: are each greater than the predetermined positive capacity threshold, or are each less than the predetermined negative capacity threshold, by using the determination obtained for a point examined before the point in progress. 